GIS − Based Logistic Regression Models for Landslide Hazard Mapping: A Case Study From Ensaro District, North Ethiopia

Abstract The present study was undertaken to identify landslides hazard prone areas in North Ethiopia. The landslide hazard in the present study area was evaluated by using the logistic regression model. Seven landslide causative factors were used for the landslide hazard evaluation, these are; slope gradient, slope aspect, elevation, proximity to streams, land-use/ land-cover, lithology and Normalized Difference Vegetation Index. Besides, for the present study landslides inventory data for the period of 2000 to 2018 was collected from the field survey and the Google earth image interpretation. The coefficient for the considered causative factors and classes were used for the identification of landslides hazard index using raster tool in ARCGIS environment. The prediction of the logistic regression model reveals that one third of the study area (32%) is under high hazard zone and the steep slopes and the elevated areas are most susceptible areas. The predicted landslides hazard zonation map is highly correlated with the training data set where 74% of it lies in the very high and high landslide hazard zones. Results of the area under the Receiver Operating Characteristic curve for the training sample, was found to be 0.76 while the area under the ROC curve of the validation sample was 0.71. Thus, the validation results has confirmed the rationality of adopted methodology, considered causative factors and their evaluation in producing LHZ map for the area. Further, the study has forwarded recommendations that can be followed to prevent and mitigate the adverse impact of landslides in the study area.